Antecedents and performance consequences of green logistics management practices: evidence from Vietnamese logistics enterprises

1 Introduction

The global economic system as of today operates under a heightened pressure on sustainable development. Indeed, climate-change adaptation strategies, carbon neutrality agreements, and several environmentally driven non-tariff barriers have together moved environmental management from a peripheral concern to a critical determinant of market access and competitiveness (Sahoo, 2024; Chaudhuri et al., 2024). In this regard, although the Environmental Kuznets Curve hypothesis suggests that eco-system degradation may eventually decouple from economic growth (Özokcu and Özdemir, 2017), firms today are facing increasingly compound demand from their stakeholders to embed sustainable initiatives into core operations, particularly supply chain and logistics which account for up to 13% of global greenhouse-gas emissions (Carter and Easton, 2011; Perotti et al., 2022).

Following which, Green Logistics Management Practices (GLMPs) have emerged as strategic instruments for effectively reducing environmental impacts while maintaining the efficiency of logistics and transportation activities (Zhu et al., 2008; Yang et al., 2013; Agyabeng-Mensah et al., 2020a). A growing body of literature indicates that GLMPs adoption is expected not only by external forces, such as institutional regulations, customer demands, and supply chain partnerships, and but also by internal ones, including both manager and employee commitment (Baah et al., 2020; Lerman et al., 2022; Sahoo, 2024). Yet, the relationship between GLMPs and firm performance (FP) remains empirically inconclusive (Agyabeng-Mensah et al., 2020a; Jayarathna et al., 2024).

Accordingly, one research stream posits that environmental integration enhances market standing and operational efficiency through value differentiation and strategic resource configuration (Vachon and Klassen, 2008; Yu et al., 2019; Danso et al., 2019). Conversely, another stream identifies inconsistent or even negative relationships between GLMPs and FP, highlighting implementation costs and technological capability as primary constraints (Feng et al., 2018; Zaid et al., 2018; Das, 2018). These mixed findings underscore the need for further research into the strategic outcomes of GLMPs, as well as the underlying mechanisms that have not been fully examined in prior studies. This need for empirical clarification is particularly pronounced in emerging economies such as Vietnam. Despite accelerating policy momentum (Ta, 2022; Anh et al., 2025) and a logistics sector projected to grow by 14%–16% annually under ambitious national sustainable development strategies (PS Engage, 2025), the specific antecedents and performance implications of GLMPs remain fragmented and underexplored.

This study thus addresses three main research gaps. First, while a growing research interest has been drawn to the foundation of green logistics adoption with environmental pressures on GLMPs being studied extensively in developed or large emerging markets (e.g., India, China), little is known about how these contextual factors interact in smaller emerging economies as Vietnam (Ngo et al., 2024; Phan et al., 2025). Second, although information traceability, now enabled via innovative technologies such as Blockchain, Big Data and IoT, are widely suggested to improve supply chain resilience and transparency, as well as sustainable performance (Agyabeng-Mensah et al., 2021; Hader et al., 2022; Razak et al., 2023), few empirical studies have probed whether and how Green Traceability (GTR) amplifies the impact of GLMPs on FP, particularly in term of Market performance (MPer) and Operational performance (OP) (Cousins et al., 2019; Moreira et al., 2022). This can be attributed to the fact that prior research has predominantly investigated the influence of GLMPs on corporate financial performance in isolation or an aggregated economic pillar of sustainability (Hong et al., 2018; Habib et al., 2020; Wang et al., 2020; Agyabeng-Mensah et al., 2021; Junaid et al., 2022). This financial-centric approach not only offers limited utility in fulfilling stakeholders’ needs and is inherently constrained by a certain degree of temporal lag, but it may also overlook how GLMPs affect other facets of FP, namely, operational efficiency and market competitiveness, thereby capturing our third research gap.

Accordingly, this study aims to advance the understanding of the antecedents of GLMPs adoption and its subsequent impact on FP, particularly MPer and OP, within Vietnamese logistics enterprises. Through the integrated theoretical lenses of Structuration Theory, Stakeholder Theory, and the Natural Resource-Based View (NRBV), this study examines how external pressures (economic pressure, customer pressure, governmental regulation) and an internal driver (managerial commitment) shape GLMPs adoption. While Structuration Theory and Stakeholder Theory explain how external institutional and stakeholder pressures translate into environmentally oriented practices, the NRBV provides the theoretical foundation for understanding how such practices subsequently enhance a firm’s performance (Hart, 1995; Hart and Dowell, 2011; Andersen, 2021). Empirically, this study offers empirical evidence on the drivers and performance implications of GLMPs within a rapidly developing context of Vietnam’s logistics sector. Furthermore, the study introduces GTR as a moderator, indicating how information transparency can affect the trajectory of green logistics endeavors. By doing so, it looks to extend the NRBV framework by demonstrating the role of a traceability-based capability in converting eco-logistics practices into tangible performance outcomes. Collectively, these insights offer strategic guidance for managers to develop green value chains and for policymakers operationalizing net-zero and circular economy transition pathways.

The remainder of the paper is organized as follows. Section 2 presents the theoretical foundation and hypothesis development. Section 3 details the research methodology. Section 4 reports the empirical findings, and Section 5 discusses these findings in light of recent empirical work, presents implications, and concludes.

2 Literature review and hypothesis development

2.1 The natural resource-based view (NRBV)

The NRBV, proposed by Hart (1995), emphasizes the role of nature-related capabilities in driving corporate growth and competitiveness. The NRBV extends the Resource-Based View (Wernerfelt, 1984; Barney, 1991), which argues that competitive advantage stems from internal resources that are valuable, rare, inimitable, and non-substitutable. While such notions have been criticized for neglecting external natural-resource constraints and opportunities, the NRBV was established to explicitly acknowledge the interdependence between firms and the natural environment. It posits that environmental stewardship serves as a foundation of sustained competitive advantage, and that firms must reconfigure their internal resource base to incorporate ecological considerations (Hart, 1995; De Stefano et al., 2016; Andersen, 2021).

The NRBV is built on three interconnected strategic capabilities: Pollution Prevention, Product Stewardship, and Sustainable Development. Pollution Prevention emphasizes minimizing emissions and waste at the source to reduce operational costs; Product Stewardship extends this focus by integrating stakeholder expectations across the product life cycle; and Sustainable Development centers on the long-term disassociation of operations from ecological degradation (Hart, 1995; Hart and Dowell, 2011).

Recent applications of the NRBV continue to underscore its explanatory power in the field of ESG. Andersen (2021) demonstrates that a relational NRBV, incorporating supplier-side green innovation, drives differentiation advantage in small manufacturing firms. In the logistics context, McDougall et al. (2022), Jayarathna et al. (2024) and Domfeh et al. (2025) use the NRBV to argue that eco-friendly operations constitute dynamic capabilities that can translate into sustainability performance. Similarly, Sahoo (2024) confirms that NRBV-consistent capabilities, namely, green data analytics, green knowledge management, and technological innovativeness, can mediate the effect of stakeholder pressure on environmental performance. These studies collectively justify the use of the NRBV as theoretical foundation of GLMPs and FP in the present study.

2.2 The theory of structuration

Proposed by Giddens (1984), Structuration Theory examines the recursive relationship between the actions of individual actors and the structures of the social system in which they operate. Social structures are conceptualized as both the medium of action and its consequence, also known as the duality of structure (Giddens, 1984). In other words, while social structures shape the behavior of actors, those same actors possess the agency to reconstitute the structure itself. Put alternatively, structures consist of rules (e.g., norms and obligations) and resources that might constrain or enable interaction among various actors; actors, in turn, can leverage their knowledge to interpret and exploit these rules and resources.

In the business context, Structuration Theory has been used to explain the drivers of corporate environmental initiatives. Lai and Wong (2012) emphasize the resource dimension reflected by the economic incentives, government subsidies, tax credits, and access to green finance, enables environmental management. This stream of reasoning has gained salience recently given the proliferation of decarbonization policy instruments (Hunjra et al., 2024). From the firm’s side, environmental stewardship not only mitigates legal exposure but also strengthens customer engagement, supplier trust, and brand legitimacy under increasingly stringent ESG disclosure requirements (Sahoo, 2024). Indeed, based on the Theory of Structuration, Sarason et al. (2020) argued that whether sustainable business ventures are successful is determined by the extent to which their actions are consistent with the strongest structures embedded in their operating society.

2.3 The Stakeholder Theory

According to Stakeholder Theory, stakeholders are groups or individuals who are affected by, and can also exert pressure on, a firm’s decisions and operations (Freeman, 2010). Decisions to engage in sustainability-oriented activities therefore involve a wide range of internal and external constituents whose expectations must be managed and addressed reasonably. Accordingly, internal stakeholders are primarily the firm’s human capital, employees and management. In sustainability-led strategies, employee engagement is widely seen as a prerequisite for long-term success, but it depends largely on top-management commitment, whose values and leadership determine organizational priorities (Lai and Wong, 2012). External stakeholder pressure stems from customers, regulators, non-governmental organizations (NGOs), and community groups; among these, regulatory and governmental pressure is frequently referred to as the most significant since non-compliance carries both legal and reputational costs (Adomako et al., 2023; Sahoo, 2024). Recent applications confirm that pressures from customers, governments, suppliers, and NGOs jointly shape green practices adoption across manufacturing, logistics and transportation (Baah et al., 2020; Baah et al., 2021a; Tetteh et al., 2025).

2.4 Green logistics management practices (GLMPs)

Although interest in green logistics has expanded rapidly, a unified definition remains far from achieved. Scholars typically characterize green logistics as an evolution of traditional logistics that integrates economic objectives with environmental responsibilities (Sbihi and Eglese, 2010; Lai and Wong, 2012; Vachon and Klassen, 2006). Indeed, Vachon & Klassen (2006) define it as a suite of supply chain strategies designed to mitigate the impacts of material flows on the environment, while Blanco and Sheffi (2017) frame it as the systematic measurement and analysis of these impacts. More recent work in the post-pandemic era has emphasized that green logistics now spans circular economic initiatives as well as Industry 4.0 levers and digital transparency mechanisms (Tetteh et al., 2024; Lu et al., 2023).

Following which, in this study, GLMPs are conceptualized as a set of environment-related strategies and operating principles, encompassing Green Strategy (GS), Green Warehousing (GW), Green Transportation (GT), Green Packaging (GP), Green Logistics Information Systems (GLIS), and Reverse Logistics (RL), applied across logistics decisions to reduce ecological footprints while preserving operational efficiency (Agyabeng-Mensah et al., 2020b; De Souza et al., 2022; Tetteh et al., 2025). This conceptualization aligns with Baah et al. (2020), who define GLMPs as a comprehensive set of management activities governing inbound and outbound physical flows that require cross-firm coordination, and with Khan et al. (2020), who emphasize green procurement, green product design, and reverse logistics within the value chain. A similar approach was also found in Vietnam, as Anh et al. (2025) operationalize GLMPs through GT, GW, GLIS, GP, and RL.

2.5 Hypothesis development

2.5.1 Customer pressure and green logistics management practices adoption

Customer Pressure (CP) refers to the demands imposed by key customer groups on a firm to mitigate the environmental and social impacts of its operations (Ateş et al., 2012; Ehrgott et al., 2011; Lai and Wong, 2012). These demands originate from customers’ rising environmental consciousness and are manifested into purchasing criteria, supplier scorecards, and ESG-related procurement standards (Kolk and Pinkse, 2022; Adomako et al., 2023). Contemporary research argues that green logistics management has become a critical factor in defining relationships between logistics providers and customers, driven particularly by the emergence of stringent international regulations and bilateral trade frameworks such as the Carbon Border Adjustment Mechanism (CBAM) and the European Union-Vietnam Free Trade Agreement (EVFTA) (Chu et al., 2019; Tetteh et al., 2025).

In fact, research posits that GLMPs have gradually become critical determinants of customer decisions to switch to offerings from green-certified providers, who comply with environmental regulations and pursue pollution reduction. For example, Lai and Wong (2012) provided empirical evidence that GLMPs adoption is a decisive factor for business customers when selecting upstream logistics partners. By adopting GLMPs, firms can also expect to generate a spillover effect that attracts new environmentally conscious customers whose values align with sustainable consumption (Cheng and Lai, 2009; Adomako et al., 2023). This has accelerated the pursuit of environmental certifications such as ISO 14001 among logistics firms (Jum’a et al., 2025). In Vietnam, where the logistics industry plays an important role in the global supply network and has reached a market value of approximately USD 40–42 billion in 2024 (EBC Financial Group, 2025), adopting GLMPs is now framed as one of the strategic movements to retain access to environmentally regulated export markets. We thus propose:

H1 CP has a positive impact on the adoption of GLMPs in Vietnamese logistics enterprises.

2.5.2 Economic pressure and green logistics management practices adoption

According to the NRBV, Economic Pressure (EP) can be characterized through two interrelated mechanisms: dematerialization (reducing natural resource consumption per unit of output) and decoupling (sustaining productivity growth while reducing reliance on natural resource inputs) (Lai and Wong, 2012). Recent macroeconomic events, such as volatile fuel prices, geopolitical tensions, and rising carbon-pricing instruments, have played a significant role in the intensification of EP, compelling logistics enterprises to use resources efficiently and to minimize ecological inputs in order to contribute to development of the circular economy and preserve their competitive positioning (Zhu et al., 2010; Seroka-Stolka and Ociepa-Kubicka, 2019). Adopting GLMPs allows firms to transition to more eco-friendly inputs or material treatment methods, reducing reliance on natural resources and negative ecological footprints.

Empirically, Ali et al. (2022) identify resource efficiency as a leading dimension of logistics service quality, while Froio and Bezerra (2021) and Jayarathna et al. (2024) argue that effective green resource management is central to strategic execution and competitive positioning. In Vietnam, recent work also confirms a strong shift toward resource optimization, process innovation, and circular-economy-based workflows to mitigate environmental dependence and logistics costs, which still stand at 16%–18% of GDP, significantly higher than regional peers (Dang and Yeo, 2018; Vu, 2023; EBC Financial Group, 2025). The following hypothesis is developed:

H2 EP has a positive impact on the adoption of GLMPs in Vietnamese logistics enterprises.

2.5.3 Governmental regulation and green logistics management practices adoption

Governmental Regulation (GR) is defined as the pressure exerted by state authorities that is reflected in legal mechanisms, such as principles and standards, that introduce constraints or incentives for sustainable corporate behavior (Hwang et al., 2016; Aboelmaged, 2018). In recent years, this regulatory stringency has been further amplified by the institutionalization of global environmental frameworks in recent years, such as the Paris Agreement, the IMO’s Net-Zero Framework, and the CBAM, which have collectively escalated environmental compliance thresholds for both domestic and international commerce. Within emerging economies, including Vietnam, governments increasingly deploy a combination of mandates, fiscal subsidies, and tax incentives to accelerate this green transition (PS Engage, 2025; Hunjra et al., 2024).

A growing body of literature identifies regulatory compliance as a primary driver of corporate green initiatives globally. This baseline has been established across diverse geographical contexts: pioneered by Petts et al. (1999) and Kammerer (2009) in European settings; demonstrated by Lai and Wong (2012) in Chinese manufacturing; and consistently reaffirmed by Zhang W. et al. (2020) and Yang and Wang (2025), as a significant predictor of green practice adoption within emerging markets. In Vietnam, despite a relative lack of robust empirical evidence, the formalization of the nation’s COP26 net-zero commitment by 2050 (via Decision No. 876/QD-TTg) on the green transport transition, coupled with the new National Strategy for Logistics Services 2025–2035, has effectively served as both environmental regulation and guidance for logistics firms (PS Engage, 2025). Consequently, it can be believed that:

H3 GR has a positive impact on the adoption of GLMPs in Vietnamese logistics enterprises.

2.5.4 Managerial commitment and green logistics management practices adoption

When managers recognize green operations as both essential and strategically beneficial, organizational demand for adopting GLMPs rises (Sroufe, 2003; Zhang Q. et al., 2020). Recent work confirms that top management commitment (MC), denoting senior managers’ environmental awareness, willingness to allocate resources and alignment with ESG standards, is a pivotal determinant of successful environmental program execution (Nazir et al., 2024; Fülöp and Cifuentes-Faura, 2026). In addition, Khan and Qianli (2017) and Sahoo (2024) both found that managers’ commitment and absorptive capacity shape how stakeholder pressure is converted into green capabilities; conversely, lack of managerial support is repeatedly identified as a major barrier to effective green-logistics implementation (Cosimato and Troisi, 2015; Jayarathna et al., 2024). In Vietnam, several studies have confirmed the impact of MC on GLMPs adoption in domestic enterprises (Le, 2020; Ta, 2022; Nishitani et al., 2021). Given the country’s organizational culture characterized by high power distance and collectivism (Nguyen and Truong, 2016), MC is expected to exert a particularly strong effect. In this regard, we hypothesize that:

H4 MC has a positive impact on the adoption of GLMPs in Vietnamese logistics enterprises.

2.5.5 Green logistics management practices adoption and Market Performance

This study follows Agyabeng-Mensah et al. (2020a) to define Market Performance (MPer) as the measure of the degree to which GLMPs adoption can satisfy customer expectations, thereby enhancing market growth, brand and customer equity. Consistent with the NRBV, by aligning operations with global sustainability standards, a logistics firm can differentiate itself from competitors through superior natural resource utilization (Sahoo, 2024; Jum’a et al., 2025). As regards, the integration of GLMPs fosters a credible green image, increasingly indispensable for accessing new domestic and international markets characterized by carbon tariffs or environmental polity (Chen, 2008; Rong and Xu, 2022; Chang et al., 2023). This was found in Vietnam by Nguyen (2016), who highlights that domestic enterprises are striving to integrate into global green supply chains to enhance market access. Additionally, and Anh et al. (2025) also demonstrate that green initiatives significantly enhance market-based outcomes. Therefore, it is possible that:

H5 The adoption of GLMPs has a positive impact on the MPer of Vietnamese logistics enterprises.

2.5.6 Green logistics management practices adoption and operational performance

According to Porter and van der Linde (1995) and King and Lenox (2001), the adoption of GLMPs often necessitates a trade-off between environmental management and operational productivity. However, contemporary research challenges this trade-off perspective, suggesting that GLMPs adoption significantly enhances firm performance (Green et al., 2012; Agyabeng-Mensah et al., 2020a; Agyabeng-Mensah et al., 2020b; Agyabeng-Mensah et al., 2020c; Agyabeng-Mensah et al., 2020d; 2021; Baah et al., 2020; 2021a; b). As stated by Green et al. (2012), GLMPs aims to refine business operations to align with stringent environmental requirements. From an organizational standpoint, the integration of GLMPs has been empirically proven to improve OP metrics such as quality, cost efficiency, on-time delivery, capacity utilization, and market positioning. Indeed, these practices facilitate superior decision-making regarding product and equipment selection, shorten lead times, minimize production waste, and bolster competitive opportunities within international markets (Sroufe, 2003; Lai and Wong, 2012; Zhu et al., 2013). In this regard, we develop the hypothesis as follows:

H6 The adoption of GLMPs has a positive impact on the OP of Vietnamese logistics enterprises.

2.5.7 The moderating effect of Green Traceability

Green-oriented enterprises frequently encounter information asymmetry with their supply chain actors (Wowak et al., 2016). Consequently, to amplify the impact of GLMPs on organizational performance, GTR emerges as a critical strategic instrument. Within the scope of this research, GTR is defined as the systematic tracking and monitoring of products and activities throughout the supply chain, from suppliers to end consumers. Enabled by Industry 4.0 technologies such as Blockchain, Big Data and IoT, this mechanism is designed to bolster corporate efforts toward achieving environmental and social collaboration while simultaneously enhancing overall firm image and performance (Hastig and Sodhi, 2020; Razak et al., 2023). Indeed, GTR provides real-time data that facilitates the detection and rectification of production anomalies (Hastig and Sodhi, 2020), thereby ensuring strict adherence to environmental standards and regulations (Rábade and Alfaro, 2006). Furthermore, according to Vachon and Klassen (2008) and Chavez et al. (2013), such transparency-based collaboration with suppliers contributes significantly to manufacturing performance, as manifested in enhanced green product quality, reduced lead times, and increased operational flexibility. Ultimately, by bolstering information transparency through robust tracking and tracing capabilities, firms can expect to achieve superior operational efficiency (Hastig and Sodhi, 2020) and strengthen the cooperation between supply chain members by establishing a mechanism for green verification (Zhang W. et al., 2020; Lee and Klassen, 2008).

H7 GTR positively moderates the relationship between GLMPs adoption and MPer of Vietnamese logistics enterprises.

H8 GTR positively moderates the relationship between GLMPs adoption and OP of Vietnamese logistics enterprises.

Based on the research hypotheses developed above, the proposed conceptual model is synthesized and summarized in Figure 1 below.

3 Research methods

3.1 Measurement of constructs

Measurement scales for all latent constructs (see Appendix) were adapted from prior research and assessed on a five-point Likert scale (1 = strongly disagree; 5 = strongly agree). Items for CP and EP were derived from Lai and Wong (2012); items for GR were adapted from Zhu and Sarkis (2007) and Dzikriansyah et al. (2023); and items for MC were drawn from Choi and Zhang (2011). Items for MPer and OP were respectively adapted from Agyabeng-Mensah et al. (2020a) and Lai and Wong (2012). GLMPs were operationalized as a higher-order construct comprising six first-order dimensions, GS, GW, GT, GP, GLIS, and RL, drawing on Agyabeng-Mensah et al. (2020a), Lai and Wong (2012), and Anh et al. (2025). Lastly, the items for GTR were adapted from Agyabeng-Mensah et al. (2021). Notably, following the adaptation of measurement items from previous studies, the first version of the questionnaire was developed in English before being translated into Vietnamese by two English-proficient translators using the back-translation method. The authors were closely involved throughout the translation process to ensure the questionnaire’s face validity, clarity, and contextual relevance.

Prior to the final survey administration, the wording of all items was revised to align with the research context and objectives of the present study. This process began with a pre-test involving two groups of experts: (1) academic experts, including professors, associate professors, and PhD holders whose knowledge and expertise were relevant to the research topic; and (2) managers occupying key leadership positions across ten Vietnamese logistics enterprises, including General Directors, Executive Directors, Branch Directors, and Heads of Finance. To conduct the pre-test, a semi-structured questionnaire comprising both closed-ended items and sections for expert feedback on the measurement scales was employed. The interviews were conducted between October and November 2023. Following Chaudhuri et al. (2024) and Nekmahmud et al. (2022), the measurement scales were refined based on the experts’ feedback to enhance item clarity, readability, and contextual appropriateness for the Vietnamese logistics industry. In addition, a pilot test involving 20 respondents from 10 Vietnamese logistics enterprises was conducted to further establish the reliability and validity of the survey instruments. Specifically, commonly used statistical techniques were applied during the pilot phase to identify and eliminate redundant items, thereby ensuring the feasibility and robustness of the full-scale survey.

3.2 Data collection

The target population comprises logistics enterprises operating in Vietnam, identified through the 2023 directory of the Vietnam Logistics Business Association (VLA). Convenience and snowball sampling were combined to maximize access to qualified respondents. For sample size, the study followed the “10-times rule” widely used in PLS-SEM (Hair et al., 2019; Barclay et al., 1995): ten times the largest number of formative indicators measuring a single construct, and ten times the largest number of structural paths directed at a single latent construct. These criteria yielded minimum thresholds of 70 and 40, respectively. Primary data were collected through an online survey hosted on the Google Forms platform between December 2023 and May 2024. Notably, participation in the survey was entirely voluntary. Additionally, prior to completing the instrument, respondents were informed of the research purpose and assured of the strict confidentiality and anonymity of their responses. Furthermore, they were not required to provide personal identifying information, with the exception of basic demographics.

To mitigate non-response bias and minimize the potential effects of common method bias (CMB), the authors contacted over 120 enterprises and attempted to collect at least two responses from each participating firm by distributing the questionnaire to managers occupying different positions within each firm. However, as the survey was administered anonymously, individual responses cannot be traced back to specific firms. Therefore, the unit of analysis in this study is the individual manager rather than the firm and responses are treated as independent observations at the individual level, consistent with preceding survey-based research in logistics and supply chain management (Shahzad et al., 2024). Due to the relatively low response rate, follow-up emails were sent after the initial contact. On average, responses were received approximately 1 month after the invitation had been distributed. Following a rigorous data-cleaning process, we removed inconsistent and unreliable responses, yielding a final sample of 183 valid questionnaires from the original 193 for further analysis.

The demographic characteristics of the respondents and their respective firms are summarized in Table 1. Specifically, most respondents were department managers and general directors, accounting for 37.16% and 24.59% of the sample, respectively. These positions suggest that the participants possessed substantial knowledge of and involvement in the implementation of GLMPs within their enterprises, as well as valuable insights into their influence on performance outcomes. The respondents also demonstrated considerable professional experience in logistics-related positions. Notably, the majority had between five and 10 years of experience (34.43%) or above (32.24%). With respect to the surveyed firms, most of them operated in transportation services, particularly maritime transportation (56.83%), inland waterway transportation (47.54%), and road transportation (45.36%). Overall, the sample reflected substantial diversity in logistics service provision, thereby enhancing the robustness and comprehensiveness of the subsequent analyses. Regarding business types, joint-stock companies and limited liability companies constituted the predominant enterprise types, representing 38.25% and 33.33% of the sample, accordingly.

TABLE 1. The characteristics of respondents and their firm.

3.3 Data analysis

The hypotheses were tested using PLS-SEM in SmartPLS 4.0. PLS-SEM was selected for its flexibility in handling complex models with multiple constructs and its robustness under non-normality and moderate sample sizes (Hair et al., 2019). The analysis proceeded in two stages: assessment of the measurement model, followed by assessment of the structural model. Because the first-order constructs are reflective, the measurement model was evaluated to check the reliability of the constructs, using indicator reliability and internal consistency, as well as the convergent and discriminant validity. To examine the moderating role of GTR, as suggested by Hair et al. (2019), after the latent variable score for GLMPs was obtained from the measurement model in the first stage, the interaction term (GLMPs × GTR) was created by multiplying these latent variable scores in the second stage, which were then used to estimate the moderating effects on MPer and OP. After establishing the reliability and validity of first- and second-order constructs, the structural model was tested. To establish the statistical significance of path coefficients, non-parametric bootstrapping with 5,000 resamples was performed, with p-values and t-statistics as primary evaluative criteria.

4 Results and discussion

4.1 Assessment of the measurement model

4.1.1 Assessment of the first order measurement model

GLMPs were modeled as a higher-order construct comprising six first-order dimensions: GS, GW, GT, GP, GLIS, and RL (Figure 2). Following Hair et al. (2021), the reflective measurement model was evaluated on indicator reliability, internal consistency, convergent validity, and discriminant validity.

Regarding indicator reliability, after initial analysis some items were dropped due to outer loadings (λ) below the 0.708 threshold. As reported in Table 2, the λ values for retained items exceed 0.708 in nearly all cases. The indicator GS2 (λ = 0.660) was retained because both Composite Reliability (CR) and Average Variance Extracted (AVE) for its construct met the recommended thresholds and the indicator did not compromise model integrity (Hair et al., 2021). Internal consistency was confirmed through Cronbach’s α and CR: all scales exceeded α = 0.70, with GP, GW, GLIS, and GT exceeding 0.80, and CR values ranging from 0.850 to 0.912. Convergent validity is established by AVE values between 0.587 and 0.838, all above the 0.50 threshold.

TABLE 2. The reliability and validity assessment of first-order measurement model.

Discriminant validity for the first-order measurement model was evaluated using the Heterotrait-Monotrait (HTMT) ratio (Table 3), a criterion widely considered more rigorous and robust than the traditional Fornell-Larcker criterion (Henseler et al., 2015). While most HTMT ratios were below the conservative threshold of 0.85, the GW-GS pair exhibited an HTMT value of 0.916, exceeding the commonly recommended threshold of 0.90. In addition, the GT-GP (0.884) and GT-RL (0.870) pairs exceeded 0.85 but remained below 0.90. Following Henseler et al. (2015) and Hair et al. (2021), bootstrapped confidence intervals were therefore examined to assess discriminant validity inferentially. As reported in Table 4, all upper confidence interval bounds remained below 1.0, indicating that discriminant validity was acceptable despite the elevated GW-GS HTMT value.

TABLE 3. The discriminant validity assessment of first-order measurement model (HTMT criteria).

TABLE 4. Bootstrapped HTMT confidence intervals for the first-order measurement model.

4.1.2 Assessment of the second order measurement model

The second-order model for GLMPs and the remaining latent variables is shown in Figure 3. To ensure methodological robustness, both the repeated-indicators and two-stage approaches were applied, addressing the limitations of each individual technique (Hair et al., 2019; Sarstedt et al., 2019).

After eliminating MC5, all remaining constructs met the reliability requirements outlined by Hair et al. (2021) (Table 5). Outer loadings for the higher-order GLMPs construct range from 0.779 to 0.891. Antecedents (CP, EP, MC, GR), performance outcomes (MPer, OP), and GTR all exhibit loadings above 0.70. Cronbach’s α exceeds 0.70 and CR exceeds 0.80 for all scales; AVE exceeds 0.50 for all constructs, confirming convergent validity.

TABLE 5. The reliability and validity test for the second-order measurement model.

For the higher-order model framework, most HTMT values were below the 0.90 threshold recommended by Gold et al. (2001), as detailed in Table 6. However, several construct pairs were close to or slightly above this threshold, including GLMPs-EP, MC-EP, MC-MPer, GR-MPer, and OP-MPer. Therefore, bootstrapped confidence intervals were further examined to assess discriminant validity inferentially. As reported in Table 7, most upper bounds of the 95% bias-corrected bootstrap confidence intervals were below 1.0. However, the MC-EP pair had an upper confidence interval of 1.003, marginally exceeding the recommended cutoff. Given that the exceedance is very small, the evidence generally supports discriminant validity, but the results should be interpreted with caution. Overall, the corrected HTMT results indicate acceptable, though not unequivocal, discriminant validity for the second-order measurement model.

TABLE 6. HTMT point estimates for the second-order measurement model.

TABLE 7. Bootstrapped HTMT confidence intervals for the second-order measurement model.

4.2 Assessment of the structural model

4.2.1 Assessment of hypotheses and structural path

Bootstrapping with 5,000 resamples produced the structural-model results in Table 8. With one exception of H1, all hypotheses are supported at p ≤ 0.05. Accordingly, EP exerts the strongest positive effect on GLMPs (β = 0.480; p < 0.001), supporting H2. GR (β = 0.269; p = 0.037) and MC (β = 0.256; p = 0.001) also positively predict GLMPs, supporting H3 and H4. CP, however, is not significantly associated with GLMPs (β = 0.125; p = 0.155), so H1 is rejected.

TABLE 8. The assessment of structural model.

Regarding performance outcomes, GLMPs significantly enhanced both MPer (β = 0.360; p < 0.001) and OP (β = 0.306; p = 0.001), supporting H5 and H6. With respect to the moderating role of GTR, as reported in Table 8, GTR significantly moderates the relationship between GLMPs and MPer (β = 0.136; p = 0.013) and between GLMPs and OP (β = 0.166; p < 0.001), supporting H7 and H8 respectively. Visually, as illustrated in Figure 4, the simple slope plots confirm that the positive effect of GLMPs on both MPer and OP is strongest when GTR is high (GTR at +1SD), moderate when GTR is at the mean, and weakest when GTR is low (GTR at -1SD). These positive moderation effects indicate that the relationship between GLMPs adoption and both MPer and OP becomes stronger as the level of GTR increases. Put in practical terms, this implies that logistics firms with higher levels of GTR are better positioned to convert their green logistics investments into tangible market and operational performance gains. Conversely, firms with lower levels of GTR may not fully realize the performance benefits of GLMPs adoption, as information asymmetry along the supply chain limits the visibility and verification of green practices.

4.2.2 Assessment of goodness-of-fit (GOF)

Table 9 reports R ² and predictive metrics. The antecedents explain 74.4% of the variance in GLMPs, while GLMPs explain 75.1% and 64.9% of the variance in MPer and OP, respectively, indicating substantial to moderate explanatory power (Henseler et al., 2015). Effect sizes (f²) indicate a medium impact of EP on GLMPs and smaller impacts of GR and MC on GLMPs. Furthermore, GLMPs exert medium effects on MPer (f² = 0.246) and OP (f² = 0.226). The moderating effect of GTR is moderate, with f² values ranging from 0.096 to 0.102. PLSpredict Q² values exceed zero for all endogenous constructs, indicating predictive relevance beyond a naïve benchmark model (Hair et al., 2021). The RMSE and MAE values reported in Table 9 provide additional transparency regarding the model’s predictive accuracy. Nevertheless, positive Q²predict values reflect predictive relevance relative to a simple mean-based benchmark rather than constituting strong evidence of absolute predictive performance (Hair et al., 2021).

TABLE 9. Goodness-of-fit (GOF).

GLMPs->MPer;

GLMPs -> OP;

GLMPs*GTR-> MPer;

GLMPsGTR -> OP*.

5 Discussion and conclusion

5.1 Discussion

This study tested an integrated model of the antecedents and performance consequences of GLMPs among Vietnamese logistics enterprises, with GTR as a moderator. The findings yield five substantive insights, each of which is discussed below in comparison with prior empirical work. First, EP emerges as the strongest antecedent of GLMPs adoption (β = 0.480, p < 0.001). This finding aligns with the “it pays to be green” perspective (Zaman and Shamsuddin, 2017; Li et al., 2021) and extends it to an emerging economy logistics setting. Nevertheless, the result contrasts with the original framing in Lai and Wong (2012), where green costs were treated primarily as a burden under EP. This can be explained by the fact that, in the post-pandemic Vietnamese context with severe fuel-price volatility, increasing logistics overheads, and stringent integration into international trade agreements, firms have come to recognize GLMPs as key capabilities to reduce fuel, packaging, and waste expenditures while improving long-term efficiency (NetZero.VN, 2026). Indeed, the Vietnam Logistics Report 2022 reports that nearly 80% of domestic enterprises have integrated green logistics into their strategies (Ministry of Industry and Trade, 2022), confirming that sustainability has become a competitive strategy rather than a discretionary cost or symbolic compliance.

Second, both GR and MC exert significant positive effects on GLMPs adoption, consistent with Stakeholder Theory and Structuration Theory. The GR effect (β = 0.269, p = 0.037) is best understood in the implementation of Vietnam’s COP26 net-zero pledge (via Decision No. 876/QD-TTg) on the transition to green transportation, and the National Strategy for Logistics Services 2025–2035 that aims to reduce the usage of unrenewable energy (PS Engage, 2025; NetZero.VN, 2026); the development of these instruments aligns well with patterns found by Nazir et al. (2024) and Lin and Ho (2011) in other Asian countries. The effect of MC (β = 0.256, p = 0.001) is supported by Lin and Ho (2011) and Zhang and Yang (2016): when senior managers possess high environmental awareness, they commit resources and cultivate a sustainability-oriented culture that motivates employees and partners to adopt environmental initiatives. In Vietnam, leadership commitment is also, in fact, recognized as a more reliable foundation for green practices than external stakeholder pressure (Nishitani et al., 2021).

Third, and most strikingly, the hypothesis on CP (H1) was rejected (β = 0.125, p = 0.155). This result is quite different from what was argued by the Stakeholder Theory and empirical findings derived from developed-market evidence (Sarkis et al., 2010; Lai et al., 2012; Chu et al., 2019; Adomako et al., 2023) but align with Lin and Ho’s (2011) early findings in China and emerging Vietnamese evidence (Ministry of Industry and Trade, 2023). Accordingly, a potential rationale for this result is that the respondents in this study were predominantly third-party logistics (3PL) providers whose customers have not become concerned with environmental issues until recent years. This can be fostered by the fact that logistics procurement within the domestic Vietnamese market is still primarily outsourced based on cost-minimization criteria rather than environmental metrics. Nevertheless, these conditions are expected to be improved in the foreseeable future when standardized and ESG-related B2B procurement scorecards diffuse deeper downstream, a phenomenon currently gaining significant traction under the escalating pressure of carbon tariffs such as the CBAM (NetZero.VN, 2026).

Fourth, the study confirms that GLMPs significantly enhance both MPer (β = 0.360) and OP (β = 0.306). Drawing from the NRBV, these findings support the proposition that GLMPs constitute strategic green resources that foster organizational capabilities (Hart and Dowell, 2011; Andersen, 2021; Jayarathna et al., 2024). Operationally, practices such as reverse logistics, transportation, and warehouse energy management reduce waste-related penalties and unit costs (Tetteh et al., 2025). From a market perspective, GLMPs function as signaling instruments that build a responsible image, increasingly critical for firms gaining customer loyalty and access to highly regulated markets such as the EU and the US under CBAM and other border-carbon mechanisms. Notably, while the positive effect on OP is consistent with the findings of Agyabeng-Mensah et al. (2020a), Agyabeng-Mensah et al. (2021) and Cousins et al. (2019), our findings regarding MPer diverge from these precedents. Accordingly, most of these researchers attributed their non-significant MPer results to the lag effect of green investments. Conversely, in Vietnam, GLMPs implementation has been pioneered by many companies as a prerequisite for participating in certain domestic and regional trade networks, capitalizing on governmental incentives and free trade agreements (Narayan and Nguyen, 2016; Tram and My, 2021; Tran, 2024).

Fifth, GTR significantly moderates both the influence of GLMPs on MPer (β = 0.136; p < 0.05) and OP (β = 0.166; p < 0.05). This is a particularly noteworthy finding because it contradicts prior studies in European contexts, namely, Cousins et al. (2019) and Moreira et al. (2022), which found the moderating role of GTR to be insignificant. A plausible rationale for this divergence is that since transparency infrastructure is already extensive, incremental traceability investments yield diminishing returns in developed countries. In Vietnam, meanwhile, transparency and GTR remain in the nascent stages; therefore, employing blockchain, big data or IoT (Habib et al., 2020; Wang et al., 2020; Jum’a et al., 2025) empowers domestic firms not only to detect flaws and acquire operationally actionable insights but also to provide transparent verification for their stakeholders (Zhang W. et al., 2020).

5.2 Conclusion

This study investigated the antecedents and performance consequences of GLMPs in Vietnam’s emerging logistics sector. By integrating Structuration Theory, Stakeholder Theory, and the NRBV, the study tested a structural model on survey data from 183 senior managers from Vietnamese logistics enterprises. The results show that EP, GR, and MC significantly drive GLMPs adoption while CP exerts no significant effect. Accordingly, EP emerged as the strongest antecedent, indicating that Vietnamese logistics firms prioritize green transitions when environmental benefits intersect with profitability. Additionally, the positive and significant moderating role of GTR confirms that green transparency is the operational mechanism converting environmental initiatives into competitive advantage in both terms of MPer and OP.

5.2.1 Managerial and policy implications

From a managerial perspective, the empirical dominance of EP implies that environmental initiatives, and GLMPs in particular, must no longer be treated as mere compliance costs; rather, they should be structurally embedded into the firm’s strategy as a mechanism to navigate and adapt to the increasingly disruptive global supply chain. Furthermore, given that MC emerges as an indispensable internal driver, senior executives should demonstrate an explicit, active commitment to environmental stewardship. This could be operationalized through proactive capital budget allocation, the institutionalization of ESG-linked performance objectives, and the cultivation of a sustainability-oriented culture. In addition, forging strategic partnerships with academic institutions and international logistics networks, such as the International Federation of Freight Forwarders Associations (FIATA) and the ASEAN Federation of Forwarders Associations (AFFA), to incorporate external green expertise into existing capabilities (Lin and Ho, 2011).

The empirical findings further highlight that successful GLMPs adoption requires a strategic paradigm shift, moving away from fragmented eco-initiatives toward a fully integrated, technology-driven framework across the entire supply chain. To operationalize this transition, corporate managers should systematically deploy Industry 4.0 technologies to establish a robust foundation for GTR, thereby mitigating pervasive information asymmetry (Wang et al., 2020; Jum’a et al., 2025). Consequently, firms can simultaneously capture internal operational efficiencies and secure external environmental legitimacy among stakeholders and international trade partners.

5.2.2 Limitation and future research direction

Several limitations create avenues for future research. First, although 183 responses are statistically adequate, participants were concentrated in specific logistics sub-sectors and were predominantly SMEs. Future research should broaden both geographic and sectoral coverage to enhance the generalizability of the findings and enable cross-country comparisons with other emerging markets. Second, the reliance on perceptual, cross-sectional survey data introduces potential common-method bias and inherently limits causal inference. Given that secondary financial data for Vietnamese logistics firms remains largely inaccessible, future studies should adopt mixed methods designs, potentially pairing survey data with objective archival financial indicators or operational metrics, to strengthen predictive power (Khoa et al., 2026). Third, this study focused on a specific, delimited set of external and internal antecedents. Future work could incorporate highly relevant country-specific drivers, such as green corporate culture, supply chain resilience capabilities, green finance access, and specific advanced Industry 4.0 enabling technologies, as either direct antecedents or moderators of GLMPs adoption. Fourth, while this study examined market and operational outcomes, future research should expand the dependent variable framework to encompass the comprehensive Triple Bottom Line, explicitly integrating environmental, social, and economic performance metrics, to better characterize the nuanced relationship between GLMPs and long-term firm and societal performance.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/ffutr.2026.1842188/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Dimitrios Nalmpantis, Aristotle University of Thessaloniki, Greece

Reviewed by: Milita Vienažindienė, Vytautas Magnus University Academy of Agriculture, Lithuania

Yasir Hussain, Minhaj University Lahore, Pakistan

Trung Pham, Hanoi University of Mining and Geology, Vietnam